Computer vision is a branch of Artificial Intelligence giving computers the ability to view the real world and make sense out of it. Among its various application areas, use of computer vision to perform precise measurement of real-world object has been explored heavily for PC as well as smartphones. For smartphones, AR technology is predominantly used for developing applications with such functionalities. However, little research has been done to explore how measurement of objects can be performed precisely in absence of devices that support AR technology. This Project was developed upon realizing that ArUco could be used to perform the same functionalities for smartphones without AR support or depth sensing cameras. Utilization of different image manipulation functions in Opencv-Contrib and other python libraries to develop a Flask API was done in order to detect object in a plain background with an ArUco marker as a reference object. The User Interface (UI) of the project was implemented using Flutter Framework. Algorithms such as the Canny Edge Detection, Circle Hough Transformation and Angle Detection using Three points from 2D image were successfully implemented in backend portion of this project. After successful implementation of such algorithms and technologies, measurements could be performed. The inability to detect objects due to presence of shadows or low contrast between object and the background was a drawback of the project. The project can serve as an alternative to physical tool for length and area estimation to individuals in mathematics or engineering background.